Current electronic health record (EHR) systems are not designed to work with genomic data, but could be modified to incorporate genomic clinical decision support (CDS) coming out of ancillary systems, says a viewpoint article in the Journal of the American Medical Association (JAMA). This approach, the authors argue, could enable doctors to benefit from the latest genomic advances without waiting for EHR vendors to catch up with this scientific revolution. But eventually, they add, EHRs will have to change.

In "Crossing the Omic Chasm: A Time for Omic Ancillary Systems," the three authors -- Justin Starren, MD, PhD; Marc S. Williams, MD; and Erwin P. Bottinger, MD -- cite a recent Institute of Medicine report noting that storage of genomic, epigenomic, proteomic and metabolomic data (the "omics") is not feasible in the current generation of EHRs. EHRs are designed to display only clinically relevant information, they point out. That's why radiological images are typically stored in picture archiving and communication systems (PACS), and only the radiology report is sent to the EHR.

While a medical image typically represents nearly 300 times as much data as the report based on it, whole-genome sequencing of a particular individual requires 5 to 10 GB of storage, 50 times more than an image. So an ancillary system is required to store that amount of data, the article says.

In addition, the authors observe, EHRs do not include the analytics needed to interpret genetic variations in light of the latest scientific research. And the genetic data will have to be stored for a long time, they point out, to reanalyze and reinterpret the genomic results in the context of evolving knowledge.

The authors predict that large organizations will likely operate their own omics ancillary systems. Small practices will probably use reference labs, which will add omics ancillary services to their current service lines.

The article offers three ways that decision support based on genomic analysis could inform medical decision making:

1. The results of the analysis could be converted to a text report that would go to the clinician.

2. "Computable observations" could be created and stored within the EHR, where the observations could be used to trigger conventional CDS rules.

3. An external CDS system could be queried by the EHR user in the clinical workflow.

In an interview with InformationWeek Healthcare, Justin Starren, chief of the division of health and biomedical informatics, department of preventive medicine, at Northwestern University Feinberg School of Medicine in Chicago, said that Northwestern is using the second option. To explain how this works, he cited a genomic-based clinical decision support tool that has been piloted at Vanderbilt University. In this project, Vanderbilt University researchers use genetic markers to predict how patients will react to the drug clopidogrel, which is used to prevent blood clotting after a stent has been inserted. If the patient's genomic data indicates that they don't have the ability to metabolize clopidogrel quickly into the active compound needed for clotting, it's recommended that they receive a different medication with a similar effect after a stenting operation.

At Northwestern, when genomic data comes in from the lab, the data is processed into the attributes that the researchers want to store, whether or not they know what those mean. Then they parse the data further to decide which of these attributes is clinically relevant. If they notice that a patient has a mutation that makes him or her a low metabolizer of clopidogrel, for example, they can send that "computed observation" to the EHR, where it can be stored in the system's observation table. Then, if clopidogrel is prescribed to a particular patient with the mutation, the EHR's CDS engine can send an alert to the doctor.

The Electronic Medical Records and Genomics (eMERGE) network is doing further research in this area with funding from the National Human Genomic Research Institute (NHGRI). The first phase of eMERGE's research, Starren noted, showed that clinical and genomic data could be combined to do scientific work. In the second phase, the consortium is continuing in the same direction, but progressing to whole-genome sequencing and the implementation of genomic CDS in the EHRs of the target sites, he said.

Regulatory requirements dominate, our research shows. The challenge is to innovate with technology, not just dot the i's and cross the t's. Also in the new, all-digital The Right Health IT Priorities? issue of InformationWeek Healthcare: Real change takes much more than technology. (Free registration required.)

RIS/EMRs/EHRs were never designed to store and manage vast amounts of data. RIS systems were the first to manifest a need to have image integration. We've come as far in that regard as to include some key images or to link to images.

As the article suggest all we can hope for at this time is to explore and develop integration options to pull the data out of those systems.

I don't expect any time soon that EHR vendors are going to re-engineer their software to accommodate managing raw genomics data. RIS systems have been around for years and they still aren't managing that data and it doesn't really make sense that they should.

We would all really appreciate one systems that does it all, but I think we will need to settle for system integration.

I agree that EHRs arenG«÷t ready to accommodate for the vast amount of data that is stored in genome sequencing and to apply the data correctly. Using these ancillary systems and services provides a great way around this. The approach outlined in the second option of having the observations created and stored with EHR and assigning triggers to the data could flow into most workflows and provide much needed help to clinicians at the point of service.